Predictive conversion systems and methods

ABSTRACT

In one embodiment, a system and method of predicting sale transaction conversion rate of an item operates through a search of information in response to a query over a network. The item can be included in a category of items. Information for other relevant items of the category is available through network query and historical data, among others. Respective information for the other items of the category is available to the method. The system and method includes discovering available information of the item of interest, extracting certain of the available information of the item, analyzing the certain information for the item by comparing the information to other item information for the category of items, weighting the information for the commercial item in comparison to other items of the category, calculating a predictive score for the commercial item of interest, and presenting the information of the commercial item of interest ranked according to the predictive score as compared to other items of the category.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/664,268, titled PREDICTIVE CONVERSION SYSTEMS AND METHODS, filed onOct. 30, 2012, which is a continuation of U.S. patent application Ser.No. 13/357,540, titled PREDICTIVE CONVERSION SYSTEMS AND METHODS, filedon Jan. 24, 2012, which is a divisional of U.S. patent application Ser.No. 12/333,124, titled PREDICTIVE CONVERSION SYSTEMS AND METHODS andfiled on Dec. 11, 2008, which claims the benefit under 35 U.S.C. 119(c)of U.S. Provisional Application No. 61/013,198, titled PREDICTIVECONVERSION SYSTEM AND METHOD and filed on Dec. 12, 2007. Each of theforegoing applications is hereby incorporated by reference herein in itsentirety, including specifically but not limited to the systems andmethods relating to predictive conversion.

BACKGROUND

Field

The present invention generally relates to search engine technology and,more particularly, to conversion rate predictors.

Description of the Related Art

As the Internet has gained in popularity, it has become an increasinglycommon means through which to discover, research, buy, sell, and rentitems. For example, it is common for individuals, retail merchants, andothers to place listings on various websites for cars, real estate,rentals, merchandise, services, and many other categories of listings.Users traditionally may browse through these listings using well knownnavigation tools. Additionally, some websites allow a user to enter asearch term or category to locate particular subjects or items ofinterest.

Typically, the searching mechanisms rely on an item listing containing areference to the search term of interest. A deficiency of such a systemis that item descriptions may be incomplete or may use a variety ofterms to describe a given aspect of the item. Accordingly, the relevanceof certain items to a search request may not be properly recognized bytraditional systems and methods.

SUMMARY

In one embodiment, a system and method of predicting sale transactionconversion rate of an item operates through a search of information inresponse to a query over a network. The item can be included in acategory of items. Information for other relevant items of the categoryis available through network query and historical data, among others.Respective information for the other items of the category is availableto the method. The system and method includes discovering availableinformation of the item of interest, extracting certain of the availableinformation of the item, analyzing the certain information for the itemby comparing the information to other item information for the categoryof items, weighting the information for the commercial item incomparison to other items of the category, calculating a predictivescore for the commercial item of interest, and presenting theinformation of the commercial item of interest ranked according to thepredictive score as compared to other items of the category.

In certain embodiments, a computer-implemented search engine method ofranking a search results list comprises receiving, in a server device, auser search request for an item, wherein the request comprises at leasta product category and a search criteria selected by the user. Thecomputer-implemented search engine method can also comprise receiving,in the server device, a content publisher preference, wherein thecontent publisher preference comprises at least a preference to maximizerevenue or a preference to maximize user experience. In certainembodiments, the computer-implemented method can also comprise searchingan inventory database for a plurality of items matching the searchcriteria, wherein a predicted conversion factor has been pre-assigned toeach of the plurality of items, wherein the predicted conversion factoris determined based on a logistic regression formula; generating, in theserver device, a list of the plurality of items based on the searching;applying, in the server device, to the conversion factor for each of theplurality of items a value to be paid by a supplier of the item, whereinthe applying is performed if the content publisher preference is tomaximize revenue; prioritizing, in the server device, the list of theplurality of items based on the conversion factor; and outputting fromthe server device the list of the plurality of items in the order basedon the prioritizing.

In certain embodiments, a computer-implemented search engine method forbuilding an inventory database that comprises receiving, in a serverdevice, inventory data for a plurality of products from at least oneinventory data source; extracting, in the server device, stated metadatafrom the inventory data for each product. The computer-implementedmethod can also comprise determining, in the server device, a productcategory or a product identification based on the stated metadata. Incertain embodiments, the computer-implemented method comprisescomparing, in the server device, for each product the product categoryor the product identification to stored metadata in a metadata database,wherein the metadata database comprises additional metadata for aplurality of products. The method can also comprise identifying, in theserver device, derived metadata for each product based on the comparing,wherein the derived metadata is metadata that is not stated metadata andis in the metadata database; generating, in the server device, aspecific conversion rate for each product based on inputting the statedmetadata and the derived metadata into a conversion rate formula,wherein the conversion rate formula is based on a logistic regressionanalysis; assigning, in the server device, the specific conversion ratecorresponding to each product; and storing in an inventory database foreach product the stated metadata, the derived metadata, and theconversion rate.

In certain embodiments, a computer-implemented search engine method forgenerating and applying a formula for calculating an expected conversionrate that comprises receiving, in a server device, stated metadata andderived metadata elements for a plurality of products that led to prioruser conversions. In certain embodiments, the computer-implementedmethod comprises determining, in a server device, the relevance of eachstated metadata and derived metadata elements in deriving expectedconversion rates, wherein the determining is based on the use of alogistic regression; generating on a periodic basis, in the serverdevice, a formula based on the determining, wherein the formula takesinto account a plurality of stated and derived metadata elements thathave been determined to be statistically relevant to expected conversionrates; and applying, in the server device, the formula to a plurality ofproducts in an inventory database to assign a predicted conversion ratefor each product in the inventory database.

In one embodiment, the system can generate a conversion rate predictionfor an item of potential commercial transaction. The system can beoperable over a communications network in conjunction with a databaseand/or bank of transaction data. In certain embodiments, the systemincludes a feed processor module in communication with the network, forformatting a discovered information about the item, data extractormodule in communication with the feed processor module, for selectivelychoosing certain of the discovered information about the item, ananalyzer module in communication with the data extractor module, forcomparing the certain of the discovered information to the transactiondata of the bank, an estimator module in communication with the analyzermodule, for weighting the discovered information as to the transactiondata of the bank, and a prioritizer module for ranking the discoveredinformation according to a result of the estimator module.

For purposes of this summary, certain aspects, advantages, and novelfeatures of the invention are described herein. It is to be understoodthat not necessarily all such advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves one advantage or groupof advantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features, aspects and advantages of theinvention are described in detail below with reference to the drawingsof various embodiments, which are intended to illustrate and not tolimit the invention. The drawings comprise the following figures inwhich:

FIG. 1 is one embodiment of a high-level block diagram illustrating aplurality of search devices in communication with a plurality of servicedevices through a network.

FIG. 2 depicts an example of one embodiment of a high-level blockdiagram illustrating a search results optimizer system comprisingvarious modules.

FIG. 3 is one embodiment of a high-level block diagram illustratingprocesses that can be performed in a search results optimizer system.

FIG. 4 depicts an example of one embodiment of a search resultsoptimizer system in communications with a plurality of supplier and/oradvertiser systems, and a plurality of users.

FIG. 5 is one embodiment of a high-level block diagram illustrating ametadata extractor module.

FIG. 6 is one embodiment of a high-level flow-chart depicting oneexample of data analysis and/or creation, and one example of optimizedsearch results generation.

FIG. 7 is a block diagram depicting one embodiment of a computer systemconfigured to run software for implementing one or more embodiments ofthe search results optimizer system illustrated herein.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Although several embodiments, examples and illustrations are disclosedbelow, it will be understood by those of ordinary skill in the art thatthe invention described herein extends beyond the specifically disclosedembodiments, examples and illustrations and includes other uses of theinvention and obvious modifications and equivalents thereof. Embodimentsof the invention are described with reference to the accompanyingfigures, wherein like numerals refer to like elements throughout. Theterminology used in the description presented herein is not intended tobe interpreted in any limited or restrictive manner simply because it isbeing used in conjunction with a detailed description of certainspecific embodiments of the invention. In addition, embodiments of theinvention can comprise several novel features and no single feature issolely responsible for its desirable attributes or is essential topracticing the inventions herein described.

For example, in response to a user search request for a product and/oritem, a conversion estimator module can be configured to conduct asearch of a product and/or item database to determine and/or obtain alist of the available products and/or items matching and/or relating tothe user search request. For each available products and/or items on theresulting and/or generated list, the conversion estimator module can beconfigured to obtain from the database corresponding weighting, ranking,and/or rating data. Based on the weighting, ranking, and/or rating data,the conversion estimator module can be configured to sort, filter, rank,and/or reorder the listing available products and/or items based on theweighting, ranking, and/or rating data associated with each productand/or item. For example, the products and/or items having highweightings, rankings, and/or ratings appear at the top of the list. Inanother example, the products and/or items having the highest weighting,ranking, and/or rating appears fourth on the list of because historicaldata may indicate that a user (and/or users) generally picks the fourthitem on the list. In another example, the weighting, ranking, and/orrating for product and/or item can be multiplied against the advertisingrevenue, conversion revenue, and/or other revenue generated when aconversion of the product and/or item occurs. Based on the modifiedweighting, ranking, and/or rating, a ranking and/or recommendationmodule can be configured to sort, filter, rank, and/or reorder thelisting available products and/or items (for example, the productsand/or items having high modified weightings, rankings, and/or ratingsappear at the top of the list).

As used herein, the terms “product,” “goods,” “service,” “inventory,”and “items” are interchangeable, and broadly refer to any category ofproducts (used or new) and/or services, including without limitationautomobiles, vacation rentals, real estate, apartments, rentals, jobs,any kind of merchandise, tickets, pets, horses, services, or the like.Examples of services include but are not limited to child-care,pet-care, elder-care, maid services, accounting, legal, programming, orthe like.

Further, the terms “metadata,” “metadata elements,” and “meta data”broadly refer to without limitation any information that describes,relates, is associated with, or is database-linked to a product, aplurality of products, or data that relates to a product. For example,metadata for a car product may include but is not limited to the color,make, model, manufacture data, accessories, CARFAX® report, ConsumerReport® data, Kelley Blue Book® report, or the like. Metadata can befurther divided into two subcategories: (i) stated metadata, and (ii)derived or implied metadata.

“Stated metadata” broadly refers to metadata that can be extracted orobtained from an original product description without referring to analternative source. For example, if a car advertisement provides themake, model, and year information, then the stated metadata is the make,model, and year information.

In contrast, “derived or implied metadata” broadly refers to metadatathat can be derived from an alternative source or can be implied fromthe product or product description even though such derived metadata isnot stated in the product description. For example, if a particularmake, model, and year of a car invariably comes with a sunroof, then thefact that the car has a sunroof is derived metadata if such informationis not stated in the product description. Other examples of derivedmetadata and/or sources for derived metadata include without limitationCARFAX® reports, Consumer Report® data, or Kelley Blue Book® reports,housing reports, job reports, location reports, quality reports, blogreports, industry reports, product reports, user reports, news reports,data derived from the Internet, provided that such data is not stated inan original product description, and/or are to be derived from a thirdparty source.

As used herein, the term “conversion” broadly refers to a click-through,a lead generation, a sale, a membership, a referral, a telephonecontact, an email notification, or other user action. For example, if auser is presented with a list of products available for purchase, andthe user clicks on one of the options, then the user click-through isbroadly referred to as a conversion, and more particularly, aclick-through conversion. Another example of a conversion is when theuser contacts the seller based on a telephone number associated with aparticular product in a listing provided to the user.

The term “logistic regression” as used herein broadly refers to a modelused for predicting the probability of an occurrence of an event, suchas a conversion. A logistic regression model makes use of severalpredictor variables that may be either numerical in nature or based oncategories. For example, the probability that a person clicks on aparticular product advertisement provided within a list of availableproducts might be predicted from knowledge of the product metadata, bothstated and derived, and/or an analysis of historical user behavior.Other names for logistic regression comprise logistic model, logitmodel, and maximum-entropy classifier. One example of a logisticregression equation is:

${\log\;{{it}\left( p_{i} \right)}} = {{\ln\left( \frac{p_{i}}{1 - p_{i}} \right)} = {\beta_{0} + {\beta_{1}x_{1,i}} + \cdots + {\beta_{k}{x_{k,i}.}}}}$

Packetized data communications networks allow information available atrespective disparate data communications devices to be discovered andviewed remotely by other network-connected communications devices.Digital data representative of such information can be maintained bynetwork-connected server computers, in conjunction with databasesoftware and hardware applications. The information discoverable in thisarrangement can be widely varied, and often includes information aboutitems being offered or otherwise promoted for sale, rental, lease, orother commercial transaction. The information can include details aboutprice, availability, location, size, make, model, color, features, andnumerous other varied characteristics of offered items.

At least certain characteristics of items offered or otherwise promotedthrough available information, such as that found on-line over wide-areanetworks, can have statistical relevance to likelihood or predictabilityof a click-through, a lead generation, and/or a sale. For example,similar makes or models of items tend to be desirable and can havesignificant predictability based on such factors as well as others. Theautomobile (including without limitation other modes of transportation)category can have characteristics of the types that will allowassessment to gain expectations about transaction success. Other items,as well, such as rentals, vacation rentals, collectibles, homes, travel,jobs, merchandise, tickets, pets, horses, services, and many others, canhave characteristics of types/categories which allow generalizedclassification and comparison, and related prediction of sale chances inview of these characteristics. Typically, these types of items can haveunique classes of characteristics (such as, for example, the make,model, any characteristics previously mentioned, or the like), and arerelatively comparable according to the classes.

Computerized search engines allow searching of disparate communicativelyconnected information sources over networks and the like. These searchengines have generally, upon input of query, searched to discoverinformation based on either keyword or natural language query. Relevantdata about a group of items under search can be collected by the searchengine, and used in performing the search in order to narrow or indexavailable information. Search results can then access and display (orotherwise made available) in some logical order. The logical order canbe a ranked presentation according to relevancy, for example, price,year, model, or the like, in the case of search of for sale items. Otherrankings can also be used by search engines in certain instances.Certain search engines charge advertisers for rank location, forexample, or more prominently portray information of select or sponsoredadvertisers/sellers and the like.

Available search information about any subject may be quite extensiveand can be available from quite diverse sources. Relevance of searchresults to keywords and subjects may not accurately reflect desires andneeds of the searcher, and particularly, this can be the case in searchfor items for sale or commercial transaction. Keywords and phrases maynot correspond well with the particular metadata collected by searchengines for indexing of information. Moreover, targeted advertising andmarketing has not generally been afforded by current technologiesbecause search rankings are merely by particular data (for example,price, model, or the like) based on searcher sort order or by preferredadvertisers of the search engine company. Predictive assessment (such asprediction of price, conversion, market timing), and buyer motivationand individuality (such as buyer's individual, unique preferences andtriggers), have not been factored.

In some embodiments, the search results optimizer system describedherein can be configured to build an inventory database based on rawproduct inventory data that can be received from suppliers and/oradvertisers. In receiving the raw product inventory data, the searchresults optimizer system can be further configured to extract theassociated metadata, stated and/or implied, that is related to theproducts, and the products and the associated metadata are stored in theinventory database. The search results optimizer system can be furtherconfigured to generate or calculate a conversion rate for each productin the inventory databases by inputting the associated metadata, statedand/or implied, for each product in a generated logistic regressionformula.

In other embodiments, the search results optimizer system is configuredto generate a logistic regression formula for a particular productcategory, wherein the logistic regression formula outputs a probabilityor a percentage likelihood that a conversion will occur, and wherein theinputs into the regression formula comprise various metadata elements,stated and/or implied, that are statistically correlated to the expectedconversion rate. In certain embodiments, the generation of the logisticregression formula is based on an analysis of historical conversionrates for products associated with specific metadata elements, statedand/or implied. The search results optimizer system can also beconfigured to cancel out, remove, add, diminish, and/or emphasizemetadata elements, stated and/or implied, as factors in the generatedlogistic regression formula, and in some embodiments such changes arebased on whether such metadata elements are more or less statisticallycorrelated to expected conversion rates. The generated logisticregression formula can be dynamically updated and/or generated on areal-time and/or periodic basis. The generated logistic regressionformula can be computer-generated or generated by entirely or partiallywith human intervention.

With reference to FIG. 4, the search results optimizer system 403 canalso be configured to receive and respond to user requests (receiveddirectly from users, and/or through a content publisher or other thirdparties) at block 420 for a listing of available products, wherein theuser request comprises one or more search terms and a product category.The search results optimizer system 403 can be configured to perform asearch of the inventory database 410 based on the one or more searchterms and the product category. In certain embodiments, the searchresults optimizer system 403 is configured to rank the results from thesearch of the inventory database 410, wherein the ranking can be basedon the conversion rate associated with each product in the results list.For example, products with a higher conversion rate are positioned atthe top of the search results list. The search results optimizer system403 can be configured to return the ranked search results list to theuser (or content publisher or other third parties) at block 420.

With reference to FIG. 4, in certain embodiments, before returning theranked list to the user, the search results optimizer system 403 isconfigured to determine whether the ranked list should be sorted basedon user experience or should be sorted to maximize revenue, wherein thedetermination can be based on the preference of the content publisher orother third party that initially received the user request. If the sortorder is based on user experience, then ranked list is ordered based onthe conversion rate. If the sort order is based on revenue maximization,the search results optimizer system 403 is configured to generate a newconversion rate by multiplying the old conversion rate for each productby the revenue paid by the supplier or advertiser (wherein the revenuedata can be stored in the revenue per conversion database 408) forgenerating a conversion for the particular product. The search resultsoptimizer system 403 can be configured to order the results list basedon the new conversion rates for each product, and return the ranked listto the user (or content publisher or other third parties) at block 420.In some embodiments, the search results optimizer system 403 can beconfigured to receive or obtain revenue paid or cost per conversioninformation or data from suppliers and/or advertisers at block 402, andstore the foregoing information or data in the revenue per conversiondatabase 408.

In reference to FIG. 1, a system 100 for predicting likelihood ofsales-lead conversion as to an item or product offered for commercialtransaction includes a communicative search device 102 in communicationwith a network 104. At least one communicative informationsource/service device 106 can also be in communication with the network104. The search device 102 is able to communicate with the informationsource/service device 106 in order to access information over thenetwork 104 available at the information source/service device 106.

The network 104 can be any communicative connection or link between thesearch device 102 and the information source/service device 106. Forexample, the network 104 can be a wide-area packetized network, such asthe Internet, an enterprise search engine, such as an intranet, personalsearch engine or mobile search engine, or other link or channel. Thesearch device 102 and the information source/service device 106 can beany computer or processing device or feature suitable for performing theoperations here described therefor. For instance, the search device 102can be a client computer, such as desktop, laptop, PDA, data-enabledphone or the like, and can be fixed or mobile and connected to or incommunication with the network by wire, wireless, or otherwise. Theinformation source/service device 106 is, for example, a server computerelectronically coupled and/or connected and/or in communication with thenetwork 104, communicatively connected by wire or other link forcommunication with network devices. The information source/servicedevice 106 includes, or has access to, an information store, such as ahardware or software database, containing information accessible overthe network 104 from the source/service device 106. The search device102 includes communicative hardware and/or software, and relatedapplications for search and access to information from the informationsource/service device 106 over the network 104. The source/servicedevice 106, together with associated database and infrastructurerequired for searchability, is herein sometimes referred to collectivelyas “search engine”.

In operation, the search device 102 queries the source/service device106 over the network 104 of the system 100, per a search request forinformation of the source/service device 106. In response to the query,the source/service device 106 makes accessible to the search device 102a result. The result typically comprises a plurality of sets ofinformation corresponding to the query. The result can be ranked for thesearch device 102 by motivational and/or conversional rate factors tothe user of the search device 102. In particular in the case of itemsoffered for sale, such as used cars, the result can be ranked for thesearch device 102 according to predicted price and predicted leadconversion rate (for example, predicted rate of sale per leads andexpected time on market until sale, or the like). Thesubjects/categories for predictive assessment can be varied according todesired implementation, application, sale item, and other factors, withranking sort dictated by applicable assessment. This yields more focusedand targeted results for each individual searching user, according topurchase motivations, conversion rates, and tendencies of theindividual.

With the system 100, an operator of the search engine can be able todictate source of its revenue generation for the search engine use andavailability through the system 100. Network advertising can becategorized as either cost per listing (CPM), cost per action (CPA), orcost per click (CPC), or the like. The search engine of the system 100,however, allows the operator to vary the revenue generation scheme forthe operator as to each respective dealer, item, class, category, and soforth. The operator is, thus, able to balance interests of advertisers,such as to make a speedy sale versus highest price, with those ofcustomers, such as customer individual and targeted interests andmotivations. These and other aspects are later elaborated.

With reference to FIG. 2, a predictive system 200, such as the system100 of FIG. 1, includes a feed processor module 202. The feed processormodule 202 discerns information available to the system 200 responsiveto search inquiries over a network (not shown in detail in FIG. 2, butshown in FIG. 1). The feed processor module 202 accesses informationfrom available sources, and then converts the feeds into a useableformat for information for the system 200. For purposes herein, the term“Vcells” can be used to identify such information formatted for thesystem 200 as to each particular product, item, record and/or set ofinformation. If the product and/or item of information regards a car orother product for sale, for example, the Vcell can be a set of dataaccessed by the system 200, to be formatted to a Vcell format to yieldparticular relevant pieces of the information for the system 200.Information accessed by the system 200 can be suitably formatted asaccessed or may otherwise require formatting as appropriate for thesystem 200. The feed processor module 202 discerns the information ineach event, to make the information available in searching with thesystem 200.

The feed processor module 202 can be in communication with a metadataextractor module 204. The metadata extractor module 204 ascertainsparticular types of information contained in each Vcell from the feedprocessor module 202. In particular, information of price, make, model,mileage and the like for cars can be obtained for each Vcell (forexample, item record of the feed processor module 202). The particularsubjects of the metadata information obtained via the metadata extractormodule 204 are variable and varied, depending on the desired applicationand configuration. For cars and other sales items, the metadatainformation can include information relevant to prediction of price,rate of conversion, and also details of features of each individual saleitem.

The metadata, itself, can be comprised as a record by a record organizermodule 206. The record organizer module 206 can be in communication withthe metadata extractor module 204. The record organizer module 206associates as a record for each sale item or product, the metadata forthe item or product, including, in the case of Internet sale items orproducts, the Uniform Resource Locator (URL) for a webpage on which theitem is offered and specific item product information (for example,price, make, model, or the like). This metadata so organized by therecord organizer module 206 is the “record” of an item, for use by thesystem 200 in search and predictive operations.

The record organizer module 206 can be in communication with an analyzermodule 208. The analyzer module 208 can compare item specificinformation for each item to aggregate comparative information fromother available information for pluralities of items of the type, class,category and/or features of the item. In effect, the analyzer module 208can compare other available information, including from historical,then-accessed estimated, and other sources, and associates item specificinformation to comparables.

The analyzer module 208 can be in communication with an estimator module210. The estimator module 210 can include without limitation logicalcircuits and processor, implemented in hardware and/or software. Logicaldeterminations can be made by the estimator module 210 based on system200 parameters, such as set by the system 200 operator and availablefrom the extractor module 204 and the analyzer module 208. Numerous andwide varieties of logical determinations can be made by the estimatormodule 210 operations. The estimator module 210 receives each recordapplicable to a search query, and then assesses estimated or predictedaspects regarding sale or other transaction involving transaction forthe item of the record. Feedback of the system 200 can be provided tothe estimator module 210, such as user experience inputs, sale events,and the like, and the estimator module 210 also uses the feedback inassessment and prediction. With cars and Internet sale offerings, as anexample, the estimator module 210 can be employable to ascertainlikelihood or chance that a user searcher will take next action, such asinput, further request, purchase, or the like. Further details areexplained in the examples below.

A prioritizer module 212 can be in communication with the estimatormodule 210. The prioritizer module 212 can rank each respective iteminformation record, as to other item information then accessed andotherwise available to the system 200. For example, a ranked listing canbe generated by the prioritizer module 212. The ranked listing from theprioritizer module 212 can be made accessible to the searching user, forview, display, and use in making transaction choices (for example,purchase decisions). The rank of the list via the prioritizer module 212is according to desired attributes, including estimated or predictedconsiderations as determined by the estimator module 210. Priceestimation and sale conversion rate estimation are two primary examplesof attributes for ranking for each independent item and correspondingrecord.

An experience interface module 214 allows input by the searching user asto various aspects of the use of the system 200. The interface module214 can be in communication with the estimator module 210. Inputs byuser searchers are feedback to refine, tune and update predictivecapabilities, as well as system 200 features and usability. These inputsallow assessment of system 200 features that are most desirable,estimations or predictions that are most favored by users, and purchaseand action decisions of users. The interface module 214 can input asfeedback are also employable to vary system 200 parameters and logic,including estimator module 210 operations and results.

Referring to FIG. 3, a method 300 (which can be implemented withoutlimitation in a specialized and/or general computer and/or system) ofpredicting transaction aspects includes block 302 of accessing a pool oftransaction information. The block 302 ascertains available informationregarding transaction items and details. The block 302 is performable,for example, through communications over a network by a search device tosource/service devices for such information. In particularimplementations, the predictive system 100 of FIG. 1 and the system 200of FIG. 2 perform the block 302 by search query on the Internet orthrough other network. Generally, a large pool of transactioninformation can be available from disparate sources. Search queriessomewhat narrow the information of the pool. However, wide variety ofinformation subsets of the pool can be accessed. The method 300 focusesand targets the information to specific search user and transaction, andallows pertinent ranking for transaction decision-making includingthrough predictive assessments.

Subsets of information accessed in block 302 are processed in block 304.In the processing block 304, the subsets are converted into useableformat (as previously referred to, the “Vcells”) for the method 300.Subsets to the processing block 304 can have widely varied formatting,substance and context in any instance. The subsets can be available forthe block 304 in generally suitable format or may otherwise requiresignificant conversion to suitable format. The processing block 304translates/converts the varied data to respective Vcells as to eachinformation per item. These Vcells are then capable of analysis in themethod 300.

After the processing block 304, specific data from each Vcell (forexample, the specific data is item-specific for each item fortransaction) can be extracted in block 306. The block 306 obtainsmetadata relevant to the search user query and the particularities ofthe user, the method 300, and the circumstances of the transaction. Inthe instance of a query for purchase transaction involving a used car,the metadata from block 306 can include such matters as price, make,model, mileage and other features and details for each car of each itemof Vcell. This metadata can be organized by the extracting at block 306according to item identity, as well as source of information (forexample, such as URL), and item information/details relevant for theparticular instance.

Analyzing at block 308 can be performed in the method 300 with theextracted data from the extracting block 306. The block 308 comparesitem-specific data for each item of the extraction (for example, such aseach for sale item) to comparable data. The comparable data for themethod 300 can be available from other blocks of the method 300, as wellas the analyzing block 308, feedback, historical comparators, and otherparticulars.

Estimating at block 310 can be performed based on results of the block308 of analyzing. In the estimating block 310, logical determinationsare made as to predictive values and specifics. The logicaldeterminations are made by comparison and weighting of data relevant tothe matters for prediction. Optimization of estimation at block 310 canbe variable, and can include data of greatest revenue to method 300operator, best customer experience or value, or others. Certain setparameters at block 310, for example, historical expectations, hierarchyof interest, and the like, are employable by the method 300 at block310. Additionally, canvas and comparison of subsets of information,other sources of information, historicity, and weighting are implementedfor determinations in block 308.

Prioritizing block 312 ranks items queried and discerned by method 300.The block 312 can be operable according to predictive assessments fromthe method 300, as well as user input and variables dictated by theoperator and implementation. The prioritizing block 312 allows searchuser access to lists, by rank, according to rules of in block 312.Predictive or estimated values and assessments, such as price estimationand sale conversion rates, are important to the rank generated by theblock 312.

In an inputting block 314, a search user who accesses ranked transactionlistings from the method 300 inputs to the method 300 information thatcan then be useable by the method 300 in present and later operations.For example, actions of input by the search user, inaction, next clicks,further reviews, sale consummations, and others are registered by themethod 300 in the inputting block 314. The information from the block314 affects operations of the method 300, including the estimating block310 and prioritizing block 312. Additionally, the step 314 provides tothe method 300 insight to user activities, preferences, and informationhelpful to the provider of the method 300 in tailoring and betterdelivering the method 300 to search users.

The foregoing systems, methods, embodiments and aspects are furtherunderstood in particular example implementations and configurations.

Example/Embodiment

A used car sale search can be performable by a user searcher over theInternet. An operator/provider can make accessible to the user searcherover the Internet, targeted and focused select information and rankingof the information. In particular, the operator/provider can maintain aweb server and associated database. Information of car items for salecan be available to the web server, including via the database. The webserver can include search query application for user searcher input inorder to obtain relevant results.

In operation, the user searcher (for example, customer) can input aquery to a web site accessible from the web server. The web server canobtain available car sale item information, discerns relevantinformation from the entire pool of such information, and converts therelevant information to individuals Vcells for each item for sale.Certain information can be available for Vcell formatting in readilysuitable format, whereas other information can require more extensiveparsing, sorting and operation. In any event, a Vcell can be generatedby the web server and database infrastructure for use in predictiveassessment prior to rank and availability to the customer. From therelevant Vcells, metadata can be extracted and analyzed by the webserver and database.

As to used car sales transactions, at least two predictive measures maybe important in sales. The predictive measures can include withoutlimitation price prediction and sales conversion rate prediction. Priceprediction relates to a favorable price according to the particularcustomer. Sales conversion rate prediction relates to likelihood of saleand period on market prior to sale for the particular car item ofinterest to the customer. As can be understood, both price and salesconversion rate have various factors that will affect prediction andactual realization. Each measure has relevance to car itemcharacteristics, types and features, as well as uniqueness of theparticular customer and other potential and past customers. These andother factors, although not absolute, can be helpful to prioritizationof opportunities, furtherance of transaction consummation, and timing ofresult.

In the example embodiment, the metadata extracted by the web server anddatabase, based on the customer search query and details, can beanalyzed by the web server and database with respect to comparativedata. The comparative data can include without limitation other relevantsearch discoveries, for example, historical information, userpreferences, operator/provider desired implementations and others.Weighting of the analysis details, and comparison of the details foreach searched product or item, provides insight into aspects that can beimportant to the predictive measures. For example, used car salesprediction can be derived based on sale conversion rates of similarhistorical sales data and also corresponding price and features foritems.

From such weighting, estimates are made for each sale item via the webserver and database. These estimates are then employed in ranking andprioritizing for presentation to the customer. This can maximize thecustomer interests in obtaining targeted and focused options.

The ranking and prioritizing for customers can alternatively oradditionally be derived by revenue interests of the operator/provider.In order to maximize revenue generation for the operator/provider,select revenue values are attributable to certain sale items versusother items. Other variations of revenue models can be implemented, aswell. If operator/provider revenue is important, then ranking andprioritizing may include assessments based on market time prior to salecompletion in each instance, pricing and favorability for sale close,features of respective items and impact to sale consummation, andsimilar matters. These variations are possible through varied weightingof predictive measures and other specifics of items presented tocustomers.

Generally, ranking and prioritization for revenue generation versuscustomer experience can be accomplished through implementations of thefollowing:

-   -   Where, r=revenue weighting (with r=1=maximize revenue, and with        r=0=maximize consumer experience)        ranking_score=(r*(predicted_conversion*conversion_revenue))+((1−r)*predicted_conversion)

The “ranking_score” in the foregoing is a derived prioritization factorthat can be employed. Of course, other determinations for ranking may beappropriate depending on desires for the situation, arrangement,customers, operator/provider and expectations.

When a user or customer (both can be interchangeable terms as usedherein) searches and accesses sale information in these manners, theuser's or customer's actions (and inaction) as to presented items andinformation serves as feedback for refinement of the operations. Thefeedback can include such interests as similarities of users orcustomers and items, historical data gathering, user preferences, and anumber of other details relevant to present and subsequent customers.The feedback can also be useful to refine website and databaseoperations, customer presentation, and other operations of theoperator/provider.

Other models and variations are possible in the used car saleapplications. The operator/provider may choose to make “recommendations”to the purchasing customer, ad placement or special display may be asource of added advertising revenues, selected features or models can beseparately or uniquely presented, and similar choices. Further, theembodiments are suitable for operation in conjunction with and asadjunct to other presently employed on-line marketing and sales toolsand practices, for example, targeted advertising, further offerings ofrelevance or tendency, and other practices on-line.

Example/Embodiment

Another example application of the foregoing is for offerings ofvacation rentals. Similar embodiments of on-line searching and customerpresentation are possible with these offerings. Vacation rentals differ(from used cars, for example) in quantification of predictive measures,however, and have greater specificity to location, property distinctcircumstances, unique qualities, and availability for particular dates,among other things. Availability, in particular, tends to have highrelation to customer choice in certain embodiments.

Although vacation rental offerings can differ as to measure predictions,the differences are primarily accountable through relevancy of data,weighting of factors for each item, and customer expectations. Customerexperience (for example, basically similar to customer expectations) canbe of greater value for prediction of sale conversion rates and price.Availability for rental can be a quite significant value for thepredictions.

As with the other embodiments and the used car example, pools ofinformation regarding rentals are discerned. Information can beformatted as Vcells, and relevant metadata can be extracted. Weighteddeterminations are made, and prioritization and ranking presents focusedand targeted information to the customer. Weighting determinations candiffer from other examples, for example, availability can besignificant. Also, location (for example, ocean view, beach access) andamenities (for example, tennis, golf) can be critical. Price can beimportant to the consumer, but may not be so important to conversionestimation rates in at least some instances.

Other Embodiments

The embodiments utilize information, then-accessible and alsohistorical, to prioritize and rank. Revenue generation desires of theoperator/provider can have different considerations, however, these areaccountable through estimations in similar manners. Numerous othervaried, similar and different examples are possible. Many users canbenefit from prediction of customer experience and conversion rates,when implemented in desirable manner in accordance with the foregoingexamples.

In the foregoing examples, data mining and similar information discoverypractices are utilized to discover information for customer results.Statistical qualification and quantification are employed to discern,segregate and weight for prioritization and ranking, according topredictive measures and others. The embodiments can prepare data inaccordance with statistical principals. In particular, the embodimentsaccount for missing data as to items or subsets. The missing data can beaddressable by discarding particular data that is not contained in anyitem or subset of information. In discarding particular data, linearregression or classification models can be employed. Specific data, suchas price data, or other specific data about items can be discardedwithout effect to other data discovered about an item or subset. Also,as may be appropriate in certain instances, entire item or subsetinformation can be discarded if missing and would skew or adverselyaffect statistical analysis. Other options include defaulting of datawhere the particular data is not necessarily critical to predictiveestimations made through the embodiments. Zip code information, forexample, may not be critical in some instances, and rather thanperforming regression or other analysis as to the particularinformation, the data can default to value not considered for otherpurposes of the embodiments.

Other particular data discovered as to items or subsets can beinconsistent or out of relevant bounds when viewed with respect to othersuch data for other items or subsets. Examples include instances ofdiscovered data that can be alternately encoded by available sources ofthe information. A four wheel drive vehicle might be encoded as “4WD”,“4×4” or other. Logic of the embodiments either recognize these asequivalent and accordingly compensate, or otherwise handlenon-recognizable inconsistencies.

Estimated or prescribed values can be employed in replacement of certainspecific erroneous or “out of bounds” data in certain instances. Amisrepresented value, for example, extra numbers or letters ininformation, could affect predictions and other measures by theembodiments if considered for assessments. Thus, statistical methods canbe employable in the embodiments to account for and avoid concerns fromsuch data.

Although other statistical procedures can be employed in theembodiments, linear regression analysis of fields of interest ofspecific data can be suitable. In the linear regression analysis, thespecific data relevant to and employed in predictive evaluation can beassessed. Account is taken to address any data that is not desirableand/or would yield inaccuracies.

Once regressions are performed with specific data of relevant fields forthe predictive measures, these data are refined and quantitized.Rankings are performed by weighting according to sale items, products,and/or offerings. Historical, as well as currently available, data andfeedback data are used in the analysis, as deemed appropriate by theoperator/provider maintaining the web server and database. Weightings inaccordance with the foregoing embodiments use all or some of the factorsof significance to predict measures of price and sales conversion rates.Relative weightings are as implemented by the operator/provider fordesired interests, including without limitation operator/providerrevenue generation and customer experience.

Search Results Optimizer System

FIG. 4 depicts an example of one embodiment of a search resultsoptimizer system 403 in communications through a network with aplurality of supplier and/or advertisers and/or corresponding systems402, and a plurality of users or corresponding user systems 420. Incertain embodiments, the search results optimizer system 403 can be anembodiment of the source/service device 106 of FIG. 1, whereas the usersor corresponding user system 420 can be an embodiment of the searchdevices 102 of FIG. 1. In certain embodiments, the supplier and/oradvertisers and/or corresponding system 402 provides, transfers,transmits, and/or routes advertising and/or supplier data on areal-time, substantially real-time, periodic, batch, and/or delayedbasis to the search results optimizer system 403. In certainembodiments, the users and/or corresponding user system 420 provides,transfers, transmits, routes and/or routes (directly or indirectlythrough a third party search engines, content publishers, or otherentities) search requests or search criteria to the search resultsoptimizer system 403.

With reference to FIG. 4, in certain embodiments, search resultsoptimizer system 403 can comprise a feed processor module 404, metadataextractor module 406, a revenue per conversion database 408, a databasefor inventory, metadata, and/or conversion data 410, a conversionestimator module 416, a ranking and/or recommendation module 418, ananalytics module 412, and a conversions module 414. In certainembodiments, the database 408 and/or the database 410 can be a singledatabase storing multiple tables, and/or each can comprise a pluralityof databases.

In reference to FIG. 4, the feed processor module 404 can be configuredto receive product inventory data (raw, formatted, processed, and/orotherwise) from one or more suppliers and/or advertisers, and/orcorresponding systems 402. The feed processor module 404 can beconfigured to convert the product inventory data into a useable and/orcompatible format for further processing by search results optimizersystem 403. In certain embodiments, the search results optimizer system403 can be configured to process, format, and/or convert the productinventory data into a Vcell format or configuration (for example, asdiscussed herein with reference to FIG. 2). The feed processor module404 can also be configured to store and/or save, with or withoutprocessing or formatting, the product inventory data into the databasefor inventory, metadata, and/or conversion data database 410.

As illustrated in FIG. 4, the feed processor module 404 can be coupledor be in communication with a metadata extractor module 406. Themetadata extractor module 406 can be configured to extract, ascertain,interpret, determine, retrieve, analyze, identify, locate informationcontained in the product inventory data and/or Vcell from the feedprocessor module 404 (for example, as discussed in reference to FIG. 2).In certain embodiments, the metadata extractor module 406 can beconfigured to use the product inventory data to generate, obtain,extrapolate, and/or supplement with derived/implied metadata (forexample, as discussed in connection with FIG. 5).

With reference to FIG. 4, the revenue per conversion database 408 can beconfigured to receive and/or store data from suppliers and/oradvertisers and/or corresponding systems 402. In certain embodiments,the data received from suppliers and/or advertisers can include withoutlimitation the revenue generated from and/or amount paid by thesuppliers and/or advertisers for generating a conversion for theproduct, and/or the cost per conversion. The data received fromsuppliers and/or advertisers can also include without limitationsupplier/advertiser a plurality of advertisement/supplier data (forexample, graphics, video, audio, text, or the like), miscellaneousinformation (for example, product availability, location, features,discounts, or the like); preference data (for example, whichadvertisement is preferred by the advertiser/supplier), or the like.

In reference to FIG. 4, the analytics module 412 can be configured toreceive, analyze, process, interpret, and/or reformat user searchrequests (either directly from the user or through a third party contentpublishers or other third parties) 420. The requests may include withoutlimitation a search for and/or a listing of available products, whereinthe user request comprises one or more search terms, a product category,a make, a model, a price or price range, a year, and/or othercharacteristic. The analytics module 403 may use, filter, combine,analyze, interpret the user request data with the data from the database410 for inventory, metadata, and/or conversion data to determine,generation, and/or analyze historical user behavior associated withrelated user searches. The analytics module may additionally send userrequest information to a conversions module 414 that can be additionallyin communication with the database 410.

With reference to FIG. 4, in certain embodiments, the conversions module414 can be configured to receive conversion data from users and/orcorresponding user systems 420. Conversion data can include withoutlimitation whether the user clicked on a product, or generated a leadfor the advertiser/supplier, produced a sale, or the like. Theconversions module 414 can be configured to analyze and/or filter theoriginal user request in conjunction with the conversion data, and otherhistorical data and/or parameters from the user or other users (obtainedfrom database 410), to identify, determine, extrapolate, and/orcalculate (for example, using a logistic regression analysis asdescribed herein) the characteristics of the user request and/or thecharacteristics of the product that correlate, predict, and/or indicatewhether a conversion would likely occur. The conversions module 414 canbe configured to use the foregoing analysis to generate and/or adjustproduct characteristic weighting factors stored in the database 410.

For example, if the conversions module 414 reviews, analyzes, and/orprocesses a user request for a car in conjunction with the conversiondata derived from the user's actions, and the historical data of otherusers performing a similar search request, and determines, filters,and/or calculates that red cars leads to a greater number ofconversions, then the conversions module 414 can be configured toincrease the weighting factor for the car characteristic “red cars” thatis stored in database 410. In another example, if the conversion module414 reviews, analyzes, and/or processes a user request for programmingjobs in conjunction with the conversion data derived from the user'sactions, and the historical data and/or parameters of other usersperforming a similar search request, and determines, filters, and/orcalculates that the term “executive” in the job title leads to a greaternumber of conversions, then the conversions module 414 can be configuredto increase the weighting factor for the job characteristic “executive”that is stored in database 410.

In reference to FIG. 4, the conversions estimator module 416 can beconfigured to receive user search request 420. In response to usersearch requests 420, the conversion estimator module 416 can beconfigured to perform, conduct, and/or search the database 410 foravailable products and/or inventory that matches and/or relates to theuser search request. The conversion estimator module 416 can also beconfigured to obtain, locate, and/or retrieve weighting, ranking and/orrating data corresponding to the products and/or inventory that matchesand/or relates to the user search request. In certain embodiments,weighting, ranking and/or rating data correlates and/or relates to thepredicted conversion rate of the product and/or inventory. For example,the higher the weighting, ranking and/or rating data, the more likely aconversion will occur for the product or inventory. In certainembodiments, the conversion estimator module 416 can be configured tosort, filter, rank, and/or reorder the listing of the products and/orinventory that matches and/or relates to the user search request basedon the weighting, ranking, and/or rating data. In certain embodiments,the reordered listing of the products and/or inventory is transferred,transmitted, and/or sent to the user, content publisher, or other thirdparty 420. In certain embodiments, the reordered listing of the productsand/or inventory is transferred, transmitted, and/or sent to the rankingand/or recommendation module 418.

For example, in response to a user search request for pet dogs, theconversion estimator module 416 can be configured to conduct a search ofthe database 410 to determine and/or obtain a list of the available petdogs matching and/or relating to the user search request. For eachavailable pet dog on the resulting and/or generated list, the conversionestimator module 416 can be configured to obtain from the database 410corresponding weighting, ranking, and/or rating data. Based on theweighting, ranking, and/or rating data, the conversion estimator module416 can be configured to sort, filter, rank, and/or reorder the listingavailable pet dogs based on the weighting, ranking, and/or rating dataassociated with each pet dog. For example, the pet dogs having highweighting, ranking, and/or rating appear at the top of the list. Inanother example, the pet dog having the highest weighting, ranking,and/or rating appears fourth on the list of because historical data mayindicate that a user (and/or users) generally picks the fourth item onthe list.

With reference to FIG. 4, the ranking and/or recommendation module 418may use data from the conversion estimator module 416 and the revenueper conversion database 408 to perform an optional secondary sorting,filtering, ranking, and/or reordering of the search results beforesending the results list to the user 420. In certain embodiments, theranking and/or recommendation module 418 can be configured to determinewhether the ranked list should be sorted based on user experience orshould be sorted to maximize revenue, wherein the determination can bebased on the preference of the content publisher or other third partythat initially received the user search request. If the sort order isbased on user experience, then the ranked list can be ordered based onthe conversion rate, weighting, ranking and/or rating data. If the sortorder is based on revenue maximization, the ranking module 418 can beconfigured to perform a secondary sorting, filtering, ranking, and/orordering for the product listing by generating a new conversion rate,weighting, ranking and/or rating to be associated with the product. Incertain embodiments, the new conversion rate can be derived bymultiplying the old conversion rate for each product by the revenuegenerated and/or the amount paid by the supplier and/or advertiser 402of the product. The revenue generated and/or the amount paid by thesupplier and/or advertiser can be stored in the revenue per conversiondatabase 408. The ranking module 418 can be configured to order theresults list based on the new conversion rates for each product, andreturn the ranked list to the user, content publisher, or other thirdparty 420.

Metadata Extractor Module

In reference to FIG. 5, a high-level block diagram illustrates oneembodiment of a metadata extractor module 406. Depending on theembodiment, certain of the blocks described below may be removed, othersmay be added, and the sequence of the blocks may be altered.

With reference to FIG. 5, the raw inventory data 502 can be received bythe metadata extractor module 406. For example, the raw inventory datacan be for a car, which may include without limitation a data source504, model 506, make 508, color 510, and year 512. In certainembodiments, the raw inventory data 502 can be any kind of product,good, service, item, or other kind of inventory. At block 524, themetadata extractor module 406 can be configured to normalize and/orprocess the raw data prior to extracting the stated metadata at block526. The extracted stated metadata may include without limitation, forexample, car model 506, make 508, and year 512. From the statedmetadata, a product and/or product category corresponding to the datacan be identified at block 528. At block 530, the metadata extractormodule 406 can be configured to search, mine, locate, obtain, and/orreceive, from the derived/implied metadata database 514, implied and/orderived metadata based on the product and/or product category identifiedand/or determined at block 528. The derived and/or implied metadata mayinclude without limitation, for example, Consumer Report® data 518,CarFax® data 520, and accessories data 522, among many other types orsources of derived/implied metadata discussed herein. At block 532, themetadata extractor module 406 can be configured to store, input, and/orsave in the inventory database 534 the stated metadata extracted fromthe raw inventory data, and/or the implied/derived metadata generatedfrom the derived/implied metadata database, in order for the metadata tobe accessible for user searches. In certain embodiments, the inventorydatabase 534 can be part of or can include all or some of the database410 for inventory, metadata, and/or conversion data.

High-Level Flow Chart

FIG. 6 illustrates one embodiment of a high-level flow-chart depictingone example of data analysis and/or creation, and one example ofoptimized search results generation, and how these two example systemsand methods interact with each other to generate predictive conversiondata used to produce optimized search results and/or listings.

With reference to FIG. 6, the data analysis and/or creation process canbe initiated at block 602 with the search results optimizer system 403obtaining, receiving, requesting the raw inventory data from suppliersand/or advertisers. At block 604, the search results optimizer system403 can be configured to extract stated metadata from the raw inventorydata. The search results optimizer system 403 can be configured to storethe extracted stated metadata in a product and/or metadata database 606.At block 608, the search results optimizer system 403 can be configuredto analyze the stated metadata to determine a product subset (forexample, determine the product category). Based on the productsubset/category, the search results optimizer system 403, at block 610,can be configured to compare, analyze, process, and/or combine thestated metadata with market analytics and/or derived metadata fromdatabase 636. The foregoing process can involve and/or compriseanalyzing historical user behavior data associated with user searchesrelated to the product subset/category. At block 612, the search resultsoptimizer system 403 can be configured to apply to each product in theproduct and/or metadata database 606 conversion formulas (for example,the logistic regression formulas disclosed herein) based on comparisonof the product to market analytics and metadata (stated and/or derived).By applying the conversion formulas to each product, a conversion rate,weighting, ranking, or rating can be generated for each product andstored in a database.

For example, the search results optimizer system 403 can be configuredto have a conversion formula for analyzing products to determine orgenerate a predictive value as to whether a user will click-thru and/orclick-on a product. In certain embodiments, this predictive value can bestored in a click-thru conversion database 614. In another example, thesearch results optimizer system 403 can be configured to have aconversion formula for analyzing products to determine or generate apredictive value as to whether a product will likely generate a lead(for example, a sales lead or contact information for sending the useradditional information). In certain embodiments, this predictive valuecan be stored in a lead generation conversion database 616. In anotherexample, the search results optimizer system 403 can be configured tohave a conversion formula for analyzing products to determine orgenerate a predictive value as to whether a user will likely purchase aproduct. In certain embodiments, this predictive value can be stored ina purchase conversion database 618. In certain embodiments, thedatabases 614, 616, 618 form one database with separate tables, or areapart of another database, or are separate databases. For those skilledin the art, it will be clear that other conversion formulas can beapplied to determine other predictive values.

In reference to FIG. 6, the search results optimizer system 403 can beconfigured to receive and/or obtain a user search request for a productat block 619. At block 620, the search results optimizer system 403 canbe configured to determine and/or identify a product subset based on thesearch request data. In certain embodiments, the user provides theproduct subset as part of the search request. At block 622, the searchresults optimizer system 403 can be configured to conduct a search forthe requested product in the product and/or metadata database 606. Atblock 624, the search results optimizer system 403 can be configured toobtain, retrieve, receive the related conversion data (for example, thepredictive values, conversion rate, weighting, ranking, and/or rating)from the databases 614, 616, 618 for each of the products found in thesearch. In certain embodiments, the conversion data can be obtained fromother databases not shown. At block 626, the search results optimizersystem 403 can be configured to determine the preferred ranking strategyto be applied. In certain embodiments, the preferred ranking strategy isdetermined by the content publisher or other third party that initiallyreceived the user request. In certain embodiments, the ranking strategycan be based on user experience, which allows the products with thehighest conversion rate to appear at the top of the results list. Incertain embodiments, the ranking strategy can be based on maximizingrevenue, wherein the products that in part generate the most revenue forthe conversion are positioned towards the top of the results list. Atblock 628, the search results optimizer system 403 can be configured toapply the weighting factors so that the search results can be orderedbased on the ranking strategy determined at block 626.

At block 630, the search results optimizer system 403 can be configuredto return, transmit, and/or send the ordered and/or ranked searchresults list to the user. In certain embodiments, the user's responsesto the ordered search results list (for example, which products and/oritems did the user click on, purchase, etc.) are determined at block632. At block 634, the search results optimizer system 403 can beconfigured to feed, process, return, and/or input the user responses,results and/or metadata associated with the products selected by theuser into the analytics database 636. At block 634, the search resultsoptimizer system 403 can be configured to adapt the conversion formulasapplied at block 612 based on the results and/or metadata associatedwith the products selected by the user.

Computing System

FIG. 7 is a block diagram depicting one embodiment of a computer systemconfigured to run software for implementing one or more embodiments ofthe search results optimizer system illustrated herein.

In some embodiments, the computer clients and/or servers describedherein take the form of a computing system 700 shown in FIG. 7, which isa block diagram of one embodiment of a computing system that is incommunication with one or more computing systems 403 and/or one or moreusers 722 via one or more networks 718. The computing system 700 may beused to implement one or more of the systems and methods describedherein. In addition, in one embodiment, the computing system 700 may beconfigured to perform search result optimization based the use oflogistic regression analysis involving metadata (stated and/or derived),and historical user behavior. While FIG. 7 illustrates one embodiment ofa computing system 700, it is recognized that the functionality providedfor in the components and modules of computing system 700 may becombined into fewer components and modules or further separated intoadditional components and modules.

Client/Server Module

In one embodiment, the computing system 700 comprises a search optimizermodule 714 that carries out the functions described herein withreference to the client server systems or the main server system. Thesearch optimizer module 714 may be executed on the computing system 700by a central processing unit 710 discussed further below.

In general, the word “module,” as used herein, refers to logic and/orsoftware embodied in hardware and/or firmware, or embedded in a machine,configured in a special purpose machine, or to a collection of softwareinstructions, possibly having entry and exit points, written in aprogramming language, such as, for example, COBOL, CICS, Java, Lua, C orC++. A software module may be compiled and linked into an executableprogram, installed in a dynamic link library, or may be written in aninterpreted programming language such as, for example, BASIC, Perl, orPython. It will be appreciated that software modules may be callablefrom other modules or from themselves, and/or may be invoked in responseto detected events or interrupts. Software instructions may be embeddedin firmware, such as an EPROM. It will be further appreciated thathardware modules may be comprised of connected logic units, such asgates and flip-flops, and/or may be comprised of programmable units,such as programmable gate arrays or processors. The modules describedherein are preferably implemented as software modules, but may berepresented in hardware or firmware. Generally, the modules describedherein refer to logical modules that may be combined with other modulesor divided into sub-modules despite their physical organization orstorage.

Computing System Components

In one embodiment, the computing system 700 also comprises a mainframecomputer suitable for controlling and/or communicating with largedatabases, performing high volume transaction processing, and generatingreports from large databases. The computing system 700 also comprises acentral processing unit (“CPU”) 710, which may include withoutlimitation a microprocessor. The computing system 700 further comprisesa memory 712, such as random access memory (“RAM”) for temporary storageof information and/or a read only memory (“ROM”) for permanent storageof information, and a mass storage device 704, such as a hard drive,diskette, or optical media storage device. Typically, the modules of thecomputing system 700 are in communication with the computer using astandards based bus system. In different embodiments, the standardsbased bus system could be Peripheral Component Interconnect (PCI),Microchannel, SCSI, Industrial Standard Architecture (ISA) and ExtendedISA (EISA) architectures, for example.

The computing system 700 comprises one or more commonly availableinput/output (I/O) devices and interfaces 708, such as a keyboard,mouse, touchpad, and printer. In one embodiment, the I/O devices andinterfaces 708 include one or more display devices, such as a monitor,that allows the visual presentation of data to a user. Moreparticularly, a display device provides for the presentation of GUIs,application software data, and multimedia presentations, for example. Inthe embodiment of FIG. 7, the I/O devices and interfaces 708 alsoprovide a communications interface to various external devices. Thecomputing system 700 may also include one or more multimedia devices706, such as speakers, video cards, graphics accelerators, andmicrophones, for example.

Computing System Device/Operating System

The computing system 700 may run on a variety of computing devices, suchas, for example, a server, a Windows server, an Structure Query Languageserver, a Unix server, a personal computer, a mainframe computer, alaptop computer, a cell phone, a personal digital assistant, a kiosk, anaudio player, and so forth. The computing system 700 is generallycontrolled and coordinated by operating system software, such as z/OS,Windows 95, Windows 98, Windows NT, Windows 2000, Windows XP, WindowsVista, Linux, BSD, SunOS, Solaris, or other compatible operatingsystems. In Macintosh systems, the operating system may be any availableoperating system, such as MAC OS X. In other embodiments, the computingsystem 700 may be controlled by a proprietary operating system. Theoperating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, and I/Oservices, and provide a user interface, such as a graphical userinterface (“GUI”), among other things.

Network

In the embodiment of FIG. 7, the computing system 700 is coupled to anetwork 718, such as a LAN, WAN, or the Internet, for example, via awired, wireless, or combination of wired and wireless, communicationlink 716. The network 718 communicates with various computing devicesand/or other electronic devices via wired or wireless communicationlinks. In the embodiment of FIG. 7, the network 718 is communicatingwith one or more suppliers and/or advertisers 720, and/or one or moreusers 722.

Access to the search optimizer module 714 of the computer system 700 bysuppliers and/or advertisers 720 and/or by users 722 may be through aweb-enabled user access point such as suppliers and/or advertisers' 720or users' 722 personal computer, cellular phone, laptop, or other devicecapable of connecting to and/or communicating with the network 718. Sucha device may have a browser module is implemented as a module that usestext, graphics, audio, video, and other media to present data and toallow interaction with data via the network 718. The browser module maybe implemented as a combination of an all points addressable displaysuch as a cathode-ray tube (CRT), a liquid crystal display (LCD), aplasma display, or other types and/or combinations of displays. Inaddition, the browser module may be implemented to communicate withinput devices 708 and may also include without limitation software withthe appropriate interfaces which allow a user to access data through theuse of stylized screen elements such as, for example, menus, windows,dialog boxes, toolbars, and controls (for example, radio buttons, checkboxes, sliding scales, and so forth). Furthermore, the browser modulemay communicate with a set of input and output devices to receivesignals from the user. The input device(s) may include withoutlimitation a keyboard, roller ball, pen and stylus, mouse, trackball,voice recognition system, or pre-designated switches or buttons. Theoutput device(s) may include without limitation a speaker, a displayscreen, a printer, or a voice synthesizer. In addition a touch screenmay act as a hybrid input/output device. In another embodiment, a usermay interact with the system more directly such as through a systemterminal connected to and/or in communication with the score generatorwithout communications over the Internet, a WAN, or LAN, or similarnetwork.

In some embodiments, the computing system 700 may include withoutlimitation a physical or logical connection established between a remotemicroprocessor and a mainframe host computer for the express purpose ofuploading, downloading, or viewing interactive data and databaseson-line in real time. The remote microprocessor may be operated by anentity operating the computer system 700, including without limitationthe client server systems or the main server system, an/or may beoperated by one or more of the users 722 and/or one or more of thecomputing systems. In some embodiments, terminal emulation software maybe used on the microprocessor for participating in the micro-mainframelink.

In some embodiments, suppliers and/or advertisers 720 who are internalto an entity operating the computer system 700 may access the searchoptimizer module 714 internally as an application or process run by theCPU 710.

User Access Point

In one embodiment, a user access point comprises a personal computer, alaptop computer, a cellular phone, a GPS system, a Blackberry® device, aportable computing device, a server, a computer workstation, a localarea network of individual computers, an interactive kiosk, a personaldigital assistant, an interactive wireless communications device, ahandheld computer, an embedded computing device, or the like.

Other Systems

In addition to the systems that are illustrated in FIG. 7, the network718 may communicate with other data sources or other computing devices.The computing system 700 may also include one or more internal and/orexternal data sources. In some embodiments, one or more of the datarepositories and the data sources may be implemented using a relationaldatabase, such as DB2, Sybase, Oracle, CodeBase and Microsoft® SQLServer as well as other types of databases such as, for example, a flatfile database, an entity-relationship database, and object-orienteddatabase, and/or a record-based database.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Although the foregoing systems and methods have been described in termsof certain preferred embodiments, other embodiments will be apparent tothose of ordinary skill in the art from the disclosure herein.Additionally, other combinations, omissions, substitutions andmodifications will be apparent to the skilled artisan in view of thedisclosure herein. While certain embodiments of the inventions have beendescribed, these embodiments have been presented by way of example only,and are not intended to limit the scope of the inventions. Indeed, thenovel methods and systems described herein may be embodied in a varietyof other forms without departing from the spirit thereof. Further, thedisclosure herein of any particular feature in connection with anembodiment and/or example can be used in all other disclosed embodimentsand/or examples set forth herein. Accordingly, other combinations,omissions, substitutions and modifications will be apparent to theskilled artisan in view of the disclosure herein.

What is claimed is:
 1. A computer-implemented method of generating andpresenting interactive search results, the method comprising: providing,by a computer system, a user interface that comprises functionality thatenables a user to interactively search for and select items;maintaining, by the computer system, one or more electronic data storesthat store information relating to a plurality of items each comprisinga plurality of attributes and a category, the stored informationcomprising predicted conversion factors each configured to indicate apredicted likelihood of conversion of one of the plurality of items;accessing, by the computer system via a computer network, a plurality ofnetwork-accessible feeds to obtain information related to the pluralityof items; generating, by the computer system, metadata based on theinformation obtained from the plurality of network-accessible feeds, themetadata comprising Uniform Resource Locators (URL's) for webpages atwhich the plurality of items are for sale, wherein the metadata furthercomprises stated metadata and derived metadata, the stated metadatacomprising attributes obtained from item descriptions provided by asource or promoter of items relating to the item descriptions, thederived metadata comprising attributes obtained from a third partysource, database-linking, in an electronic data store, the generatedmetadata to the plurality of items; generating, by the computer system,the predicted conversion factors by inputting the generated metadatainto a logistic regression formula, the logistic regression formulagenerated based at least in part on an analysis of historical conversionactivity and stated metadata and derived metadata related to attributesassociated with items different than the plurality of items to determinestatistically how each attribute associated with the items differentthan the plurality of items affected the historical conversion activity,the logistic regression formula configured to apply a result of theanalysis to the inputted metadata to output the predicted conversionfactors, wherein the items different than the plurality of itemscomprise a same category as the plurality of items but comprise one ormore different attribute values; receiving, by the computer system, auser search request generated via the user interface, the user searchrequest comprising an item search criteria; searching, by the computersystem, the one or more electronic data stores for a plurality of itemsrelating to the item search criteria; generating, by the computersystem, for the user a personalized user interface data that providesfunctionality for the user to select from a result set of items based onthe searching, the result set being prioritized based on the predictedconversion factors; causing, by the computer system, rendering of asearch results output, based on the personalized user interface data,the search results output presents the prioritized result set andenables interaction with, by the user, the plurality of items relatingto the item search criteria; electronically monitoring, by the computersystem, over a computer network, user interactions with the searchresults output to determine a user response to the search resultsoutput, the user response comprising at least a selection of a URLassociated with an item; adapting, by the computer system, the logisticregression formula based on the user response, the adapting comprisingat least including an indication of the selection of the URL in thehistorical conversion activity analyzed to generate the logisticregression formula; regenerating, by the computer system, one or more ofthe predicted conversion factors by inputting the generated metadatainto the adapted logistic regression formula; receiving, by the computersystem, a second search request that comprises the item search criteria;and generating, by the computer system, a second result set of itemsbased on the second search request, the second result set comprising adifferent prioritization based at least in part on the regeneratedpredicted conversion factors, wherein the computer system comprises oneor more physical servers.
 2. The computer-implemented method of claim 1,wherein the item search criteria comprises a product category.
 3. Thecomputer-implemented method of claim 1, further comprising: analyzing,by the computer system, the user search request in conjunction withhistorical data to determine one or more characteristics of the usersearch request.
 4. The computer-implemented method of claim 3, whereinanalyzing the user search request in conjunction with the historicaldata comprises applying a regression analysis.
 5. Thecomputer-implemented method of claim 3, wherein the historical datacomprises conversion data.
 6. The computer-implemented method of claim3, wherein the one or more characteristics of the user search requestare associated with item characteristics correlated with an increasedlikelihood of conversion.
 7. The computer-implemented method of claim 1,wherein the one or more electronic data stores comprise one or more ofthe following: a relational database, a flat file database, anentity-relationship database, an object-oriented database, arecord-based database, a distributed database.
 8. A computer-implementedmethod of generating and presenting interactive search results, themethod comprising: receiving, by a computer system, via a plurality ofnetwork-accessible feeds, data relating to a plurality of items eachcomprising a plurality of attributes and a category; generating, by thecomputer system, metadata based on the data received via the pluralityof network-accessible feeds, the metadata comprising Uniform ResourceLocators (URL's) for webpages at which the plurality of items are forsale, wherein the metadata further comprises stated metadata and derivedmetadata, the stated metadata comprising attributes obtained from itemdescriptions provided by a source or promoter of items relating to theitem descriptions, the derived metadata comprising attributes obtainedfrom a third party source, database-linking, in an electronic datastore, the generated metadata to the plurality of items; calculating, bythe computer system, predicted conversion factors for the plurality ofitems, the predicted conversion factors calculated at least in part byinputting the stated metadata and the derived metadata into a logisticregression formula, the logistic regression formula generated based atleast in part on an analysis of historical conversion activity andstated metadata and derived metadata related to attributes associatedwith items different than the plurality of items to determinestatistically how each attribute associated with the items differentthan the plurality of items affected the historical conversion activity,the logistic regression formula configured to apply a result of theanalysis to the inputted metadata to output the predicted conversionfactors, wherein the items different than the plurality of itemscomprise a same category as the plurality of items but comprise one ormore different attribute values; providing, by the computer system, auser interface that comprises functionality that enables a user tointeractively search for and select items; receiving, by the computersystem, a user search request generated via the user interface, the usersearch request comprising an item search criteria; searching, by thecomputer system, the electronic data store for a plurality of itemsrelating to the item search criteria; generating, by the computersystem, a result set of items based on the searching, the result setbeing prioritized based on the predicted conversion factors; causing, bythe computer system, rendering of a search results output that presentsthe prioritized result set and enables interaction with, by the user,the plurality of items relating to the item search criteria;electronically monitoring, by the computer system, over a computernetwork, user interactions with the search results output to determine auser response to the search results output, the user response comprisingat least a selection of a URL associated with an item; adapting, by thecomputer system, the logistic regression formula based on the userresponse, the adapting comprising at least including an indication ofthe selection of the URL in the historical conversion activity analyzedto generate the logistic regression formula; regenerating, by thecomputer system, one or more of the predicted conversion factors byinputting the generated metadata into the adapted logistic regressionformula; receiving, by the computer system, a second search request thatcomprises the item search criteria; and generating, by the computersystem, a second result set of items based on the second search request,the second result set comprising a different prioritization based atleast in part on the regenerated predicted conversion factors, whereinthe computer system comprises one or more physical servers.
 9. Thecomputer-implemented method of claim 8, wherein the data relating to theplurality of items is provided by a source or promoter of the pluralityof items.
 10. The computer-implemented method of claim 8, wherein theitem search criteria comprises a product category.
 11. Thecomputer-implemented method of claim 8, further comprising: analyzing,by the computer system, the user search request in conjunction withhistorical data to determine one or more characteristics of the usersearch request.
 12. The computer-implemented method of claim 11, whereinanalyzing the user search request in conjunction with the historicaldata comprises applying a regression analysis.
 13. Thecomputer-implemented method of claim 11, wherein the historical datacomprises conversion data.
 14. The computer-implemented method of claim11, wherein the one or more characteristics of the user search requestare associated with item characteristics correlated with an increasedlikelihood of conversion.
 15. The computer-implemented method of claim8, wherein the one or more electronic data stores comprise one or moreof the following: a relational database, a flat file database, anentity-relationship database, an object-oriented database, arecord-based database, a distributed database.